Faculty Candidate Seminar
Machine Learning for Accelerating Scientific Discovery
This event is free and open to the publicAdd to Google Calendar
Abstract: The dramatic increase in both sensor capabilities and computational power over the last few decades has created enormous opportunities for using machine learning (ML) to enhance scientific discovery. To realize this potential, ML systems must seamlessly integrate with the key tools for scientific discovery. For instance, how can we use ML for simulating high-dimensional data under uncertainty? How can we use ML to plan experiments under real-world budget constraints? How can we incorporate scientific domain knowledge within ML algorithms?
For the above questions, I’ll first present the key computational and statistical challenges through the lens of probabilistic modeling. Next, I’ll highlight limitations of existing approaches for scaling to high-dimensional data and present algorithms from my research that can effectively overcome these challenges. These algorithms are theoretically principled, domain-agnostic, and exhibit strong empirical performance. Notably, I’ll describe a collaboration with chemists and material scientists where we used these algorithms to optimize an experimental pipeline for electric batteries and significantly reduced the overall time costs from about 1.5 years to 16 days. Finally, I’ll conclude with an overview of future opportunities for using ML to accelerate scientific discovery.
Bio: Aditya Grover is a fifth-year Ph.D. candidate in Computer Science at Stanford University advised by Stefano Ermon. His research focuses on learning and inference algorithms for probabilistic models and is grounded in applications to accelerate physical sciences. Aditya’s research has been published in top scientific and ML/AI venues (e.g., Nature, NeurIPS, ICML, ICLR, AAAI, AISTATS), included in popular open source ML software, and deployed into production at major technology companies. His work has been recognized with a best paper award (StarAI), a Lieberman Fellowship, a Data Science Institute Scholarship, and a Microsoft Research Ph.D. Fellowship. He is also a Teaching Fellow at Stanford since 2018, where he co-created and teaches a new class on Deep Generative Models. Previously, Aditya obtained his bachelors in Computer Science and Engineering from IIT Delhi in 2015, where he received a best undergraduate thesis award.